The amount of data collected and stored by various entities is increasing year over year. The data collected, sometimes referred to as “big data”, may include data sets that may be beyond the ability of commonly used software tools to manage and process the data sets within a reasonable time period due to the data sets size. Data stores have been developed to better handle large data sets, such as non-relational databases and distributed database systems. These data stores may be used to capture and store large data sets in the areas of science, education, government and business, where persons working in these industries may encounter limitations utilizing data due to the amount of data encountered.
As data is gathered by an increasing number of computing devices and systems, opportunities to utilize the data may also increase. For example, financial institutions, such as stock exchanges, may capture and store a large amount of trading data where the trading data may contain a fine level of stock tick information for each individual stock. Because of the fine level of granularity that may be captured within a data set, various entities may wish to analyze data at various levels of granularity contained within a larger data set.